CN111901026A - Arrival angle estimation method in communication - Google Patents
Arrival angle estimation method in communication Download PDFInfo
- Publication number
- CN111901026A CN111901026A CN202010661014.3A CN202010661014A CN111901026A CN 111901026 A CN111901026 A CN 111901026A CN 202010661014 A CN202010661014 A CN 202010661014A CN 111901026 A CN111901026 A CN 111901026A
- Authority
- CN
- China
- Prior art keywords
- arrival
- arrival angle
- angle
- antenna
- array
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Radio Transmission System (AREA)
Abstract
The invention discloses an arrival angle estimation method in communication. The method comprises the following steps: acquiring a wireless signal received by an array antenna in real time; inputting the wireless signals received by the array antenna in real time to an arrival angle estimation model, and outputting a corresponding arrival angle estimation result, wherein the arrival angle estimation model is obtained by pre-training by using a training data set, and the training data set is used for representing the mapping relation between the wireless signals received by the array antenna and the arrival angle under various communication scenes. The method and the device can quickly and accurately estimate the arrival angle in the communication process, and are particularly suitable for the communication scene with quick change.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a method for estimating an arrival angle in communication.
Background
In recent years, array signal processing technology has been rapidly developed and widely applied to the fields of communication, radar, astronomy and the like. Angle-of-arrival estimation is one of the typical applications of array signal processing.
For wireless communication systems, angle-of-arrival estimation of a channel plays an important role in many fields. For example, in smart antenna systems, angle-of-arrival estimation may reduce the complexity of beamforming design. Especially for millimeter wave large-scale multiple-input multiple-output, if the arrival angle information can be obtained, the beam forming through the control vector can obtain larger gain with lower complexity in the signal arrival direction. The angle-of-arrival estimation may be performed at the user equipment in addition to at the base station. By designing a reasonable scheme, the feedback of channel state information between the base station and the user equipment can be reduced or even eliminated, thereby improving the beam training efficiency. In addition, some key technologies of fifth generation mobile communication systems also require support for angle of arrival estimation to improve performance, such as simultaneous wireless information and power transmission (swift) and non-orthogonal multiple access (NOMA).
In the prior art, the arrival angle estimation method is generally divided into two types: 1) spectral-based methods, including multiple signal classification (MUSIC) and rotational invariance (ESPRIT) techniques to estimate signal parameter techniques. 2) Parameter-based methods include space-alternating generalized expectation-maximization (SAGE) and the like. MUSIC and SAGE are not limited by the form of the antenna array, while ESPRIT requires the array sub-array to have translation invariance, so these techniques can only be applied to uniform linear or planar arrays.
The complexity of the angle-of-arrival estimation scheme is crucial to reducing device power consumption and complexity. However, the existing method needs to perform complex mathematical operations including spectral peak search, iterative calculation and the like according to the antenna receiving signal. For a scene with a rapidly changing communication environment, such as vehicle communication, the environment is often changed drastically within several seconds, and the relevant mathematical operation is difficult to complete in a short time, so that the corresponding arrival angle cannot be obtained in real time along with the change of the environment. This is because vehicle communication has the following features: both the transmitter and receiver of the vehicle communication are fast moving; the low-height vehicle-mounted antenna enables dense near scatterers to exist near the transmitting and receiving vehicles; the line-of-sight propagation between the transmitting and receiving ends can encounter frequent random blocking; vehicle communication environment changes rapidly, etc. Therefore, if the arrival angle estimation method in the prior art is directly applied to vehicle communication with rapidly changing communication environment, the method has great limitation, and the rapid and real-time estimation of the arrival angle is difficult to realize.
Disclosure of Invention
The present invention is directed to overcome the above-mentioned drawbacks of the prior art, and provides a new method for estimating a fast arrival angle, which is capable of quickly and accurately estimating a fast arrival angle in wireless communication in real time, and is particularly suitable for a scenario where a communication environment changes rapidly.
The invention provides an arrival angle estimation method in communication. The method comprises the following steps:
acquiring a wireless signal received by an array antenna in real time;
inputting the wireless signals received by the array antenna in real time to an arrival angle estimation model, and outputting a corresponding arrival angle estimation result, wherein the arrival angle estimation model is obtained by pre-training by using a training data set, and the training data set is used for representing the mapping relation between the wireless signals received by the array antenna and the arrival angle under various communication scenes.
In one embodiment, the angle-of-arrival estimation model is trained according to the following steps:
obtaining a steering matrix of the antenna array, and calculating a wireless signal received by the array antenna based on the steering matrix;
under various communication scenes, measuring a wireless signal received by an array antenna and a corresponding arrival angle in advance;
constructing a training data set by using the wireless signals received by the array antenna and the corresponding arrival angle;
using machine learning to establish a mapping relationship between the wireless signals received by the array antenna and the angle of arrival, expressed as:
f()＝ωT+b
wherein, representing the wireless signals received by the array antenna, ω and b are weights and offsets;
the arrival angle estimation model f () is obtained by solving the parameters ω and b.
In one embodiment, the wireless signals received by the array antennas in the training data set are data that have undergone dimension reduction processing on the original samples.
In one embodiment, the dimension reduction process is performed according to the following steps:
obtaining covariance matrixes of all samples in wireless signals received by a group of array antennas according to a principal component analysis theory, and decomposing eigenvalues of the covariance matrixes to obtain a plurality of eigenvalues;
taking a feature vector corresponding to one feature value as one component of the original sample;
and performing descending order arrangement on the obtained multiple eigenvalues, selecting the largest k eigenvalues, and forming a projection matrix by using eigenvectors of the k eigenvalues as training data after dimension reduction processing, wherein k is less than the number of original samples of the wireless signal.
In one embodiment, the plurality of communication scenes are urban, suburban, campus and highway communication scenes selected according to environmental characteristics.
Compared with the prior art, the method has the advantages that the trained arrival angle estimation model can be continuously used, the communication system only needs to collect the array snapshot and carry out a small amount of data preprocessing, the received signals are input into the arrival angle estimation model obtained in the off-line stage, the arrival angle can be quickly obtained, and the calculation time is low. The method can realize rapid and real-time arrival angle estimation in the actual communication process, and is particularly suitable for vehicle communication scenes.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow diagram of an angle-of-arrival estimation method according to one embodiment of the invention;
fig. 2 is an antenna array schematic according to an embodiment of the invention;
FIG. 3 is a schematic illustration of a wireless signal received by an antenna according to one embodiment of the present invention;
FIG. 4 is an illustration of an angle of arrival of a wireless signal received by an antenna in accordance with one embodiment of the present invention;
FIG. 5 is a flow diagram of an angle-of-arrival estimation model according to one embodiment of the invention;
fig. 6 is an arrival angle estimation result example 1 according to an embodiment of the present invention;
fig. 7 is an arrival angle estimation result example 2 according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In order to solve the problem that the existing spectrum-based and parameter-based methods in the communication environment are difficult to realize the rapid estimation of the arrival angle, the invention provides a novel rapid arrival angle estimation method, which models an antenna receiving signal and the arrival angle into a nonlinear mapping relation and establishes a model for describing the mapping relation, so that a complex operation process can be separated from the communication process, and a main operation process can be completed in an off-line stage; and the estimation of the arrival angle can be rapidly realized in real time according to the antenna receiving signals by utilizing the established mapping relation in the actual communication process. The principles of the present invention will be described below primarily in terms of vehicle communications, but it should be understood that the present invention is equally applicable in other communication scenarios.
In short, the technical solution for fast arrival angle estimation provided by the present invention can be generally divided into an offline training process and an online arrival angle estimation process.
With reference to fig. 1, for an off-line training process, the task is to obtain a regression model by a machine learning algorithm using a training data set containing known outputs. The method specifically comprises the following steps:
The arrival angle estimation method of the present invention is based on an array antenna, and therefore it is necessary to prepare an array antenna that can normally receive wireless signals, as shown in fig. 2.
And step 2, performing pre-measurement.
The pre-measurement is the reception of wireless signals by the array antenna in an actual vehicle communication scenario.
And 3, generating a training data set.
The training data set comprises two parts, wherein the first part is arrival angle information which can be obtained by using the existing arrival angle estimation method; the second part is the wireless signal received by the array antenna, and the wireless signal can perform preprocessing operations on the basis of step 2, such as calibration, data dimension reduction and the like.
And 4, training to obtain an arrival angle estimation model.
For example, a machine learning algorithm is used to establish a mapping relationship between the wireless signals received by the array antenna and the arrival angle, and this mapping relationship is an arrival angle estimation model. The input of the model is a wireless signal received by the array antenna, the output is an estimated arrival angle, and the training process can be carried out in a server or a cloud offline mode.
In the on-line arrival angle estimation process, the task is to rapidly obtain the corresponding arrival angle by using the wireless signals received by the array antenna in real time by using an arrival angle estimation model obtained in an off-line stage in the actual vehicle communication process. The method specifically comprises the following steps:
and 5, carrying out actual communication.
In actual communication, wireless signals can be received in real time using the same antenna array used in step 1 and step 2.
And 6, preprocessing data.
And performing data preprocessing on the wireless signals received by the array antenna, wherein the data preprocessing process is the same as that in the step 3.
And 7, estimating the arrival angle in real time.
And (4) inputting the processed wireless signals received by the array antenna obtained in the step (6) as input into the arrival angle estimation model obtained in the step (4), wherein the model output is the estimation result of the arrival angle corresponding to the input wireless signals.
For the online estimation process, the antenna array based data reception and data pre-processing process is the same as the offline training process. The difference is that the online process does not require model training, but directly uses the angle-of-arrival estimation model already obtained at the offline stage. The input of the arrival angle estimation model is preprocessed measurement data, and the output is an arrival angle estimation result. Most of the operations of the arrival angle estimation scheme provided by the invention are concentrated on the training of the estimation model, the time-consuming operations can be performed off-line, and the obtained estimation model can be continuously used once the training is completed.
To further understand the present invention, the data preprocessing process in step 3 and the process of obtaining the angle-of-arrival estimation model in step 4 are described in more detail below, i.e., how to obtain a training data set from the wireless signals received by the array antenna and how to obtain the angle-of-arrival estimation model by using the training data set.
Specifically, still referring to fig. 1, the arrival angle estimation method provided by the present invention includes the following steps:
For example, an array antenna may be used as shown in FIG. 2, where A1-A16 are 16 antenna elements, O is the origin of the coordinate system, and the xy plane is the horizontal plane. Each of the 16 antenna elements has the same radiation pattern although disposed at different positions and directions, and each element can receive a radio signal.
It should be understood that the shape and number of elements of the antenna array pattern shown in fig. 2 are only given for the purpose of explaining and explaining the technical solution in detail. All array antennas with directional antenna elements can be applied to the technical solution proposed by the present invention, and the difference in shape and number of antenna elements only affects the accuracy of the output angle of arrival.
For an array antenna with M antenna elements, the received signal x (t) of the array can be represented in the form of a matrix:
x(t)＝As(t) (1)
wherein x (t) ═ x1(t),x2(t),…,xM(t)]TAnd s (t) is the signal for all P sources:
s(t)＝[s1(t),s2(t),…,sP(t)]T(2)
where A is an M P steering matrix, each row of A
Is the steering vector for the ith source, so the complete steering matrix a can be expressed as:
wherein, in
And θ are azimuth and elevation angles, respectively, ω and τ representing phase information.
For the problem of angle of arrival estimation to be solved by the present invention, the objective is to obtain the angle of each source in s (t) under the known conditions of x (t) and a, that is, to establish the mapping relationship between the wireless signal received by the antenna array and the angle of arrival. To achieve this goal, step 1 requires obtaining a steering matrix for the antenna array.
In the embodiment of the invention, two ways of obtaining the steering vector are provided, the first way is to measure in an anechoic chamber to obtain the radiation pattern of each antenna unit, and a steering matrix can be obtained from the radiation pattern; the second method is to measure the radiation pattern of only one antenna unit, and then obtain the steering matrix of the whole antenna array by using geometric calculation according to the relative position relationship of each antenna unit. In the technical scheme provided by the invention, as long as the shape of the antenna array and the number of the antenna units are kept unchanged, the obtained guide vector can be continuously used.
And step 2, performing pre-measurement.
The pre-measurement is the reception of wireless signals by the array antenna in an actual vehicle communication scenario. The pre-measurement is intended to obtain measured data for use in generating a training data set from the measured data in step 3. For example, the pre-measurement is performed from various vehicle communication environments with the help of a set of wireless signal transceiving equipment.
For example, the transmission signal is a broadband multi-carrier signal with a bandwidth of 30MHz, a center frequency of 5.9GHz, and a transmission power of 34 dBm. The antenna array in step 1 is installed on the roof of a vehicle, the height is about 1.8 meters, and the x-axis direction of the antenna array in fig. 2 is the advancing direction of the vehicle. In order to keep the technical solution from losing generality, the present invention collects a large number of received signals in various environments, including urban areas, suburban areas, campuses, highways, etc. In these scenarios, road width, traffic density, number of buildings, and other environmental characteristics all vary. The measurement data from various environments helps to make the angle-of-arrival estimation model obtained in step 4 more representative and versatile.
It should be noted that the above-mentioned wireless signal transceiver, the testing method and parameters are only for explaining and explaining the technical solution in detail, and are not used to limit the present invention. When the test mode, scene and parameters are changed, the technical scheme is also applicable as long as the same test configuration as that in the step 3 is adopted in the step 5.
And 3, generating a training data set.
The training data set contains both the radio signal received by the array antenna and the known angle of arrival. For example, a data set may be represented as
WhereiniIs a wireless signal received by an antenna as shown in fig. 3. liIs the arrival angle information obtained using the existing arrival angle estimation method, as shown in fig. 4. Each one of which isiAngle of arrival information l corresponding theretoiA set of training data pairs is formed, s being the number of training data pairs in the training data set.
Considering that the number of wireless signal samples received per group is generally high, it is preferable to apply the wireless signal samples in step 3iAnd (5) performing data dimension reduction. Specifically, according to the principle component analysis theory, the covariance matrix XX of all samples in a certain group of received signals is obtained firstTAnd performing eigenvalue decomposition on the obtained product. Then, corresponding to one of the characteristic values λiCharacteristic vector omega ofiAnd (i ═ 1,2, …, d) is a component of the original sample. Therefore, the eigenvalues can be arranged in descending order, the largest k eigenvalues can be selected, and the eigenvectors of the eigenvalues can form a projection matrix W*＝(ω1,ω2,…,ωd). The original samples can be mapped into k-dimensional space using a projection matrix. k is generally smaller than the number of original samples of the wireless signal, so that the data dimension reduction of the wireless signal is realized.
For the arrival angle information in the training data set, the existing arrival angle estimation method can be used for solving, and details are not repeated here.
And 4, training to obtain an arrival angle estimation model.
For example, a machine learning algorithm is used to establish a mapping relationship between the wireless signals received by the array antenna and the arrival angle, and this mapping relationship is an arrival angle estimation model. The input of the model is a wireless signal received by an antenna, and the output is an arrival angle. In particular, an arrival angle estimation model may be obtained using a support vector machine theory-based or other machine learning model.
Specifically, referring to the flow of obtaining the arrival angle estimation model of fig. 5, the method includes the following steps:
step S41, problem modeling and transformation.
For the training data set containing m data in step 3
The aim of the invention is to establish a mapping between the received signal and the angle of arrival information l, which can be abstracted into a regression problem, namely:
f()＝ωT+b (4)
wherein f () is the model output, the arrival angle estimation problem can be converted to solve the model parameters ω and b, so that the model output f () is as close as possible to the arrival angle information l corresponding to the received signal. According to the existing convex optimization theory, the solution of the regression problem can be converted into the following optimization problem:
where C is the regularization coefficient, p∈Is a loss function.
And step S42, solving the optimization problem.
For example, a set of relaxation variables ξ may be introducediAnd
the above optimization problem is further expressed as:
the optimization problem can be solved by a Lagrange multiplier method, and a Lagrange multiplier alpha is introducediAnd
the lagrangian multiplier in the above optimization problem can be solved using existing methods.
In step S43, an arrival angle estimation model is obtained.
By solving the lagrange multiplier in step S42, the parameters ω and b of the regression model can be solved:
up to this point, the desired angle of arrival estimation model f () can be obtained with the model parameters ω and b known.
And 5, carrying out actual communication.
In actual communication, the wireless signal is received in real time by using the same antenna array in step 1 and step 2. The format of the received signal should be the same as step 1 and step 2.
And 6, preprocessing data.
Data pre-processing is performed on the wireless signals received by the array. The data preprocessing process is the same as the processing of the received signal in step 3. The angle-of-arrival information is calculated in step 6 without using existing methods.
And 7, estimating the arrival angle in real time.
The processed wireless signal obtained in step 6 is input to the arrival angle estimation model f () obtained in step 4, and the model output is the arrival angle corresponding to the input wireless signal. Fig. 6 and 7 are examples of the arrival angle estimation results.
In summary, the time-consuming mathematical operation of the arrival angle estimation method provided by the invention is concentrated in the off-line stage. Steps 1 to 4 of the offline phase may be performed separately in advance, and once training is completed, the arrival angle estimation model obtained in step 4 may be continuously used without changing the antenna array structure, the antenna deployment position, and the antenna radiation pattern. In the online stage, the vehicle communication system only needs to collect the array snapshot and carry out a small amount of data preprocessing, the received signal is input into the estimation model obtained in the offline stage, the arrival angle can be quickly obtained, and the calculation time consumption is low. Therefore, the method provided by the invention can realize rapid and real-time arrival angle estimation in the actual communication process.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (9)
1. An arrival angle estimation method in communication, comprising the steps of:
acquiring a wireless signal received by an array antenna in real time;
inputting the wireless signals received by the array antenna in real time to an arrival angle estimation model, and outputting a corresponding arrival angle estimation result, wherein the arrival angle estimation model is obtained by pre-training by using a training data set, and the training data set is used for representing the mapping relation between the wireless signals received by the array antenna and the arrival angle under various communication scenes.
2. The method of angle of arrival estimation in communications of claim 1, wherein said angle of arrival estimation model is trained according to the following steps:
obtaining a steering matrix of the antenna array, and calculating a wireless signal received by the array antenna based on the steering matrix;
under various communication scenes, measuring a wireless signal received by an array antenna and a corresponding arrival angle in advance;
constructing a training data set by using the wireless signals received by the array antenna and the corresponding arrival angle;
using machine learning to establish a mapping relationship between the wireless signals received by the array antenna and the angle of arrival, expressed as:
f()＝ωT+b
wherein, representing the wireless signals received by the array antenna, ω and b are weights and offsets;
the arrival angle estimation model f () is obtained by solving the parameters ω and b.
3. The arrival angle estimating method in communication according to claim 2, wherein solving the parameters ω and b to obtain an arrival angle estimation model f () includes:
setting an optimization problem:
the optimization problem is further expressed as:
s.t.f(i)-li≤∈+ξi,
ξi≥0,
solving the optimization problem by a lagrange multiplier method:
0≤αi,
solving the lagrange multiplier to obtain parameters omega and b:
where C is the regularization coefficient, ρ∈As a function of the loss, ξiAnd
as a relaxation variable, αiAnd
being lagrange multipliers, m represents the number of samples in the training dataset,iis a radio signal received by an array antenna in a training data set,/iIs the angle of arrival information in the training dataset.
4. The method of angle-of-arrival estimation in communications according to claim 1, wherein the steering matrix of the antenna array is obtained according to the steps of:
measuring the radiation pattern of each antenna unit in an anechoic chamber, and further obtaining a steering matrix from the radiation pattern; or
And measuring the radiation pattern of only one antenna unit, and obtaining the steering matrix of the whole antenna array by utilizing geometric calculation according to the relative position relation of each antenna unit.
5. The method of estimating angle of arrival in communication according to claim 1, wherein the wireless signals received by the array antennas in the training data set are data subjected to dimensionality reduction on original samples.
6. The method of angle of arrival estimation in communications according to claim 5, wherein the dimension reduction is performed according to the following steps:
obtaining covariance matrixes of all samples in wireless signals received by a group of array antennas according to a principal component analysis theory, and decomposing eigenvalues of the covariance matrixes to obtain a plurality of eigenvalues;
taking a feature vector corresponding to one feature value as one component of the original sample;
and performing descending order arrangement on the obtained multiple eigenvalues, selecting the largest k eigenvalues, and forming a projection matrix by using eigenvectors of the k eigenvalues as training data after dimension reduction processing, wherein k is less than the number of original samples of the wireless signal.
7. The arrival angle estimation method in communication according to claim 1, wherein the plurality of communication scenarios are communication scenarios of urban areas, suburban areas, campuses, and expressways, which are selected according to environmental characteristics.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
9. An electronic device comprising a memory and a processor, on which a computer program is stored which is executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented when the processor executes the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010661014.3A CN111901026B (en) | 2020-07-10 | 2020-07-10 | Arrival angle estimation method in communication |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010661014.3A CN111901026B (en) | 2020-07-10 | 2020-07-10 | Arrival angle estimation method in communication |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111901026A true CN111901026A (en) | 2020-11-06 |
| CN111901026B CN111901026B (en) | 2021-07-20 |
Family
ID=73192405
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010661014.3A Active CN111901026B (en) | 2020-07-10 | 2020-07-10 | Arrival angle estimation method in communication |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111901026B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105246086A (en) * | 2015-10-08 | 2016-01-13 | 北京邮电大学 | Method and equipment for determining antenna angles |
| CN106535133A (en) * | 2016-11-11 | 2017-03-22 | 天津大学 | Indoor telephone traffic accurate location method based on machine learning in cellular network |
| US20170227623A1 (en) * | 2013-10-01 | 2017-08-10 | Phasorlab, Inc. | Angle of Arrival Measurements Using RF Carrier Synchronization and Phase Alignment Methods |
| CN109085531A (en) * | 2018-08-27 | 2018-12-25 | 西安电子科技大学 | Near field sources angle-of- arrival estimation method neural network based |
| CN109255308A (en) * | 2018-11-02 | 2019-01-22 | 陕西理工大学 | There are the neural network angle-of- arrival estimation methods of array error |
| CN109327249A (en) * | 2018-11-13 | 2019-02-12 | 钟祥博谦信息科技有限公司 | Angle-of- arrival estimation method in mimo system |
| US20190212409A1 (en) * | 2018-01-11 | 2019-07-11 | Hon Hai Precision Industry Co., Ltd. | Methods and devices for estimating angle information for wireless communication systems |
| CN110446160A (en) * | 2019-08-13 | 2019-11-12 | 南京戎智信息创新研究院有限公司 | A kind of deep learning method of the vehicle location estimation based on multi-path channel status information |
-
2020
- 2020-07-10 CN CN202010661014.3A patent/CN111901026B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170227623A1 (en) * | 2013-10-01 | 2017-08-10 | Phasorlab, Inc. | Angle of Arrival Measurements Using RF Carrier Synchronization and Phase Alignment Methods |
| CN105246086A (en) * | 2015-10-08 | 2016-01-13 | 北京邮电大学 | Method and equipment for determining antenna angles |
| CN106535133A (en) * | 2016-11-11 | 2017-03-22 | 天津大学 | Indoor telephone traffic accurate location method based on machine learning in cellular network |
| US20190212409A1 (en) * | 2018-01-11 | 2019-07-11 | Hon Hai Precision Industry Co., Ltd. | Methods and devices for estimating angle information for wireless communication systems |
| CN109085531A (en) * | 2018-08-27 | 2018-12-25 | 西安电子科技大学 | Near field sources angle-of- arrival estimation method neural network based |
| CN109255308A (en) * | 2018-11-02 | 2019-01-22 | 陕西理工大学 | There are the neural network angle-of- arrival estimation methods of array error |
| CN109327249A (en) * | 2018-11-13 | 2019-02-12 | 钟祥博谦信息科技有限公司 | Angle-of- arrival estimation method in mimo system |
| CN110446160A (en) * | 2019-08-13 | 2019-11-12 | 南京戎智信息创新研究院有限公司 | A kind of deep learning method of the vehicle location estimation based on multi-path channel status information |
Non-Patent Citations (3)
| Title |
|---|
| AFTAB KHAN等: "Angle-of-Arrival Estimation Using an Adaptive Machine Learning Framework", 《 IEEE COMMUNICATIONS LETTERS》 * |
| MI YANG等: "Machine-Learning-Based Scenario Identification Using Channel Characteristics in Intelligent Vehicular Communications", 《IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS ( EARLY ACCESS )》 * |
| 王婉苓等: "基于时频分析的到达角估计方法比较", 《火力与指挥控制》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111901026B (en) | 2021-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Cai et al. | Dynamic channel modeling for indoor millimeter-wave propagation channels based on measurements | |
| CN106021637B (en) | DOA estimation method based on the sparse reconstruct of iteration in relatively prime array | |
| Chen et al. | Hybrid spherical-and planar-wave modeling and DCNN-powered estimation of terahertz ultra-massive MIMO channels | |
| CN106707255B (en) | phased array radar simulation system and method | |
| Zhang et al. | Application of artificial bee colony algorithm to maximum likelihood DOA estimation | |
| CN106501765B (en) | A kind of Maximum Likelihood DOA Estimation based on quadratic sum and Semidefinite Programming | |
| Cai et al. | Trajectory-aided maximum-likelihood algorithm for channel parameter estimation in ultrawideband large-scale arrays | |
| Wu et al. | Computationally efficient DOA and carrier estimation for coherent signal using single snapshot and its time-delay replications | |
| Afkhaminia et al. | 2D off‐grid DOA estimation using joint sparsity | |
| Doeker et al. | Angle of arrival and angle of departure estimation using compressed sensing for terahertz communications | |
| CN111901026B (en) | Arrival angle estimation method in communication | |
| Yang et al. | Central DOA estimation of incoherently distributed noncircular sources with cross-correlation matrix | |
| Shi et al. | Chirp-based hierarchical beam training for extremely large-scale massive MIMO | |
| Tayem | Real time implemenation for DOA estimation methods on Ni-Pxi platform | |
| Singh et al. | Machine learning based digital beamforming for line-of-sight optimization in satcom on the move technology | |
| CN114966526A (en) | Direction-of-arrival estimation method | |
| Li et al. | Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR | |
| You et al. | Exploiting angular spread in channel estimation of millimeter wave MIMO system | |
| Xu et al. | Two-dimensional direction-of-arrival estimation of noncircular signals in coprime planar array with high degree of freedom | |
| Nouri et al. | DOA Estimation based on Gridless Fuzzy Active Learning under Unknown Mutual Coupling and Non-Uniform Noise: Experimental Verification | |
| Shi et al. | Wideband DOA estimation with deficient snapshots using low rank Toeplitz structure | |
| Nxumalo et al. | Direction of arrival (DOA) estimation for smart antennas in weather impacted environments | |
| Gao et al. | 2D-DOA Estimation Based on Higher-Order SVD-Based Using EMVS Sparse Array | |
| Vaca-Rubio et al. | Floor map reconstruction through radio sensing and learning by a large intelligent surface | |
| Cheng et al. | Gibbs Sampling Based Sparse Bayesian Learning for Direction-of-Arrival Estimation with Impulse Noise Towards 6G |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |